Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph.

lgb.plot.importance(tree_imp, top_n = 10L, measure = "Gain",
  left_margin = 10L, cex = NULL)

Arguments

tree_imp

a data.table returned by lgb.importance.

top_n

maximal number of top features to include into the plot.

measure

the name of importance measure to plot, can be "Gain", "Cover" or "Frequency".

left_margin

(base R barplot) allows to adjust the left margin size to fit feature names.

cex

(base R barplot) passed as cex.names parameter to barplot.

Value

The lgb.plot.importance function creates a barplot and silently returns a processed data.table with top_n features sorted by defined importance.

Details

The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature. Features are shown ranked in a decreasing importance order.

Examples

data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) params <- list( objective = "binary" , learning_rate = 0.01 , num_leaves = 63L , max_depth = -1L , min_data_in_leaf = 1L , min_sum_hessian_in_leaf = 1.0 ) model <- lgb.train(params, dtrain, 10) tree_imp <- lgb.importance(model, percentage = TRUE) lgb.plot.importance(tree_imp, top_n = 10L, measure = "Gain")